Beta
Understanding Lego sets popularity
Now let's now move on to the competition and challenge.
📖 Background
You recently applied to work as a data analyst intern at the famous Lego Group in Denmark. As part of the job interview process, you received the following take-home assignment:
You are asked to use the provided dataset to understand the popularity of different Lego sets and themes. The idea is to become familiarized with the data to be ready for an interview with a business stakeholder.
💾 The data
You received access to a database with the following tables. You can also see above a visualization of how the tables are related to each other. (source):
inventory_parts
- "inventory_id" - id of the inventory the part is in (as in the inventories table)
- "part_num" - unique id for the part (as in the parts table)
- "color_id" - id of the color
- "quantity" - the number of copies of the part included in the set
- "is_spare" - whether or not it is a spare part
parts
- "part_num" - unique id for the part (as in the inventory_parts table)
- "name" - name of the part
- "part_cat_id" - part category id (as in part_catagories table)
part_categories
- "id" - part category id (as in parts table)
- "name" - name of the category the part belongs to
colors
- "id" - id of the color (as in inventory_parts table)
- "name" - color name
- "rgb" - rgb code of the color
- "is_trans" - whether or not the part is transparent/translucent
inventories
- "id" - id of the inventory the part is in (as in the inventory_sets and inventory_parts tables)
- "version" - version number
- "set_num" - set number (as in sets table)
inventory_sets
- "inventory_id" - id of the inventory the part is in (as in the inventories table)
- "set_num" - set number (as in sets table)
- "quantity" - the quantity of sets included
sets
- "set_num" - unique set id (as in inventory_sets and inventories tables)
- "name" - the name of the set
- "year" - the year the set was published
- "theme_id" - the id of the theme the set belongs to (as in themes table)
- num-parts - the number of parts in the set
themes
- "id" - the id of the theme (as in the sets table)
- "name" - the name of the theme
- "parent_id" - the id of the larger theme, if there is one
Acknowledgments: Rebrickable.com
💪 Challenge
Create a report to summarize your findings. Include:
- What is the average number of Lego sets released per year?
- What is the average number of Lego parts per year?
- Create a visualization for item 2.
- What are the 5 most popular colors used in Lego parts?
- [Optional] What proportion of Lego parts are transparent?
- [Optional] What are the 5 rarest lego bricks?
- Summarize your findings.
library(tidyverse)
1. What is the average number of Lego sets released per year?
Unknown integration
Data frameavailable as
Sets
variable
-- Select the 10 most popular colors from all parts
SELECT *
FROM sets
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
AvgSetsPerYear <- Sets %>%
group_by(year) %>%
summarise(year_sets = n_distinct(set_num)) %>%
ungroup() %>%
summarise(mean(year_sets)) %>%
as.numeric()
# The average number of Lego sets per year is:
AvgSetsPerYear
2. What is the average number of Lego parts per year?
PartsPerYear <- Sets %>%
group_by(year) %>%
summarise(year_parts = sum(num_parts)) %>%
ungroup() %>%
arrange(year)
AvgPartsPerYear <- PartsPerYear %>%
summarise(mean(year_parts)) %>%
as.numeric()
# The average number of Lego parts per year is:
AvgPartsPerYear
3. Create a visualization for item 2.
# Visualising Lego parts per year:
ggplot(PartsPerYear, aes(x = year, y = year_parts)) +
geom_col() +
labs(title = "Lego Parts Per Year", x = "Year", y = "Lego Parts") +
geom_hline(yintercept = AvgPartsPerYear, linetype = "longdash")
4. What are the 5 most popular colors used in Lego parts?
Unknown integration
Data frameavailable as
PartsPerColor
variable
SELECT color_id, name, is_trans, SUM(quantity) AS n_parts
FROM inventory_parts
LEFT OUTER JOIN colors ON color_id = id
GROUP BY color_id, colors.name, is_trans
ORDER BY n_parts DESC
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
FiveTopColors <- PartsPerColor %>%
head(5) %>%
select(name) %>%
pull()
# The five most popular Lego parts colors are:
FiveTopColors